https://orcid.org/0009-0004-9372-6851
Figures & data
Figure 1. Flow chart of study design and participants.
Abbreviations: eGFR, estimated glomerular filtration rate.
Table 1. Baseline characteristics of participants with and without follow-up chronic kidney disease (CKD) in the development cohort (N = 12012).
Figure 2. Forest Plots of factors of chronic kidney disease (CKD) were analyzed by multivariable logistic analysis in the development cohort.
Abbreviations: OR, odds ratio; 95% CI, 95% confidence interval; SBP, systolic blood pressure; BMI, body mass index; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; FPG, fasting plasma glucose; HbAlc, glycated hemoglobin; UA, uric acid; Scr, serum creatinine.
Figure 3. Nomogram to predict the 3-year risk of chronic kidney disease (CKD).
Abbreviations: SBP, systolic blood pressure; BMI, body mass index; TG, triglycerides; FPG, fasting plasma glucose; HbAlc, glycated hemoglobin; Scr, serum creatinine.
Figure 4. Receiver operating characteristic (ROC) curves for the 3-year chronic kidney disease (CKD) risk prediction model. AUCs in the development cohort (A) and in the validation cohort (B).
Abbreviations: AUC, area under the curve.
Figure 5. Calibration curves for predicting the risk of 3-year chronic kidney disease (CKD) in all cohorts. (A) in the development cohort; (B) in the validation cohort.
Figure 6. The decision curves analysis curve of nomogram and other factors.
Abbreviations: eGFR, estimated glomerular filtration rate; Scr, serum creatinine.
The DCA curve of our predictive model (red line) was little higher than that of the eGFR curve alone (blue line) or the age + Scr curve (green line) models. The closer the DCA curve is to the upper right corner, the greater the benefit, so our model had better clinical value and potential net benefits than other two models.
